Modifying image parameters using wearable device input

ABSTRACT

Systems and methods are presented for modifying image parameters of an image to be captured by an image capturing device based on input from a wearable computing device. In some embodiments, the system receives image data, determines an image parameter based on the image data, and receives data from a wearable computing device positioned proximate to a subject of the image. The system modifies the image parameter based on the data received from the wearable computing device and captures the image data using the modified image parameter.

PRIORITY

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 15/856,472, filed Dec. 28, 2017,which is a continuation of and claims the benefit of priority to U.S.patent application Ser. No. 15/272,983, filed Sep. 22, 2016, which is acontinuation of and claims the benefit of priority to U.S. patentapplication Ser. No. 14/581,991, filed on Dec. 23, 2014, each of whichis hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to photography.Specifically, the present disclosure addresses systems and methods formodifying image parameters in a camera device using data received from awearable computing device.

BACKGROUND

Cameras, and devices containing cameras, such as smartphones, are usedto capture images of individuals and groups. Groups take candid andposed photographs, some of which employ a time delay function of thecamera or device or a remote control device connected to the camera totrigger the capture of an image. Wearable computing devices, such assmart watches and glasses, having one or more sensors have recentlybecome popular. Wearable computing devices can be used to chartactivities, such as exercise and rest, using the one or more sensorsembedded within the wearable computing device. Although some cameras canemploy remote controls, controllers for cameras are often limited toenabling a user to remotely trigger a shutter of the camera or manuallyadjust camera settings.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a network diagram illustrating a network environment suitablefor modifying image parameters of an image to be captured by an imagecapturing device based on input from a wearable computing device,according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a device capable ofcapturing image data, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an image capturesystem capable of capturing image data with an image parameter.

FIG. 4 is a flowchart illustrating operations of an image capture systemin performing a method of determining an image parameter of an image tobe captured and modifying the image parameter based on data receivedfrom a wearable computing device, according to some example embodiments.

FIG. 5 is a flowchart illustrating operations of an image capture systemin performing a method of determining an image parameter of an image tobe captured and modifying the image parameter based on sensor datareceived from a wearable computing device and sensor data received froma sensor associated with the image capture system, according to someexample embodiments.

FIG. 6 is a flowchart illustrating operations of an image capture systemin performing a method of determining an image parameter and a capturemode of an image to be captured and modifying the image parameter andthe capture mode based on data received from a wearable computingdevice, according to some example embodiments.

FIG. 7 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods and systems are directed to modifying image parametersof an image to be captured by an image capturing device based on inputfrom a wearable computing device. For example, users with wearablecomputing devices about to get their picture taken can be incommunication with the image capturing device through the wearablecomputing device. The image capturing device can adjust automatically toget the best picture by modifying settings such as zoom, lighting,picture mode, resolution, and other image and camera parameters. Theimage capturing device can also automatically focus on subjects withinthe image to be captured based on proximity of the wearable computingdevice to the user or subject of the image. Further, example methods andsystems are directed to modifying parameters, functions, and modes of animage capture device based on input from a wearable computing device.Examples merely typify possible variations. Unless explicitly statedotherwise, components and functions are optional and may be combined orsubdivided, and operations may vary in sequence or be combined orsubdivided. In the following description, for purposes of explanation,numerous specific details are set forth to provide a thoroughunderstanding of example embodiments. It will be evident to one skilledin the art, however, that the present subject matter may be practicedwithout these specific details.

FIG. 1 is a network diagram illustrating a network environment 100suitable for modifying image parameters of an image to be captured by animage capturing device based on input from a wearable computing device,according to some example embodiments. The network environment 100includes a server machine 110, a database 115, user devices 130 and 140,and wearable computing devices 150 and 160, all communicatively coupledto each other via a network 190. The server machine 110 may form all orpart of a network-based system 105 (e.g., a cloud-based server systemconfigured to provide one or more services to the user devices 130 and140). The server machine 110 and the user devices 130 and 140 may eachbe implemented in a computer system, in whole or in part, as describedbelow with respect to FIG. 2 or 7.

In some embodiments, the server machine 110 can be a server, web server,database, or other machine capable of receiving and processinginformation, such as image data. The server machine 110 can be a portionof a social media system, website, or database. In some embodiments, theserver machine 110 can include software and hardware capable ofperforming facial recognition analysis of image data. Further, in someembodiments, the server machine 110 can include non-transitory machinereadable media containing information indicative of a user and subjectsassociated with the user. For example, the data indicative of thesubjects can include facial profiles for the subjects and the user, oneor more characteristics used for facial recognition analysis,identification data, and other suitable identifying data. In someembodiments, at least a portion of the identification data can bewearable device identification data associated with the subjects and theuser.

Also shown in FIG. 1 are users 132 and 142. One or both of the users 132and 142 may be a human user (e.g., a human being), a machine user (e.g.,a computer configured by a software program to interact with the userdevice 130), or any suitable combination thereof (e.g., a human assistedby a machine or a machine supervised by a human). The user 132 is notpart of the network environment 100, but is associated with the userdevice 130 and may be a user of the user device 130. For example, theuser device 130 may be a desktop computer, a vehicle computer, a tabletcomputer, a navigational device, a portable media device, a smartphone,or a wearable device (e.g., a smart watch or smart glasses) belonging tothe user 132. Likewise, the user 142 is not part of the networkenvironment 100, but is associated with the user device 140. As anexample, the user device 140 may be a tablet computer, a portable mediadevice, a smartphone, a wearable device (e.g., a smart watch or smartglasses), or other suitable devices belonging to the user 142.

The wearable computing devices 150 and 160 can be mobile devicesconfigured to be worn by the user 132 or 142. For example, wearablecomputing devices can be configured as a watch, glasses, a ring, or anyother suitable wearable article. The wearable computing devices 150 and160 can include one or more processor, one or more memory, one or moredisplay device, one or more input/output device, one or morecommunication device, and one or more sensor. The one or more processor,one or more memory, one or more display device, one or more input/outputdevice, and one or more communication device can be implementedsimilarly to the components described in relation to FIGS. 2 and 7below. The one or more sensor of the wearable computing device 150 or160 can be chosen from a group consisting of a light sensor, a positionsensor (e.g., GPS, altimeter, or elevation sensor), a clock, anaccelerometer, a gyroscope, a microphone (e.g., where the user device130 is configured to capture the image data as a set of imagescomprising a video), or other sensors or input devices of the wearablecomputing device 150 and 160.

Any of the machines, databases, or devices shown in FIG. 1 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software to be a special-purpose computer to perform oneor more of the functions described herein for that machine, database, ordevice. For example, a computer system able to implement any one or moreof the methodologies described herein is discussed below with respect toFIG. 7. As used herein, a “database” is a data storage resource and maystore data structured as a text file, a table, a spreadsheet, arelational database (e.g., an object-relational database), a triplestore, a hierarchical data store, or any suitable combination thereof.Moreover, any two or more of the machines, databases, or devicesillustrated in FIG. 1 may be combined into a single machine, and thefunctions described herein for any single machine, database, or devicemay be subdivided among multiple machines, databases, or devices.

The network 190 may be any network that enables communication between oramong machines, databases, and devices such as the server machine 110,the user device 130, and a wearable device. Accordingly, the network 190may be a wired network, a wireless network (e.g., a mobile or cellularnetwork), or any suitable combination thereof. The network 190 mayinclude one or more portions that constitute a private network, a publicnetwork (e.g., the Internet), or any suitable combination thereof.Accordingly, the network 190 may include one or more portions thatincorporate a local area network (LAN), a wide area network (WAN), theInternet, a mobile telephone network (e.g., a cellular network), a wiredtelephone network (e.g., a plain old telephone system (POTS) network), awireless data network (e.g., WiFi network or WiMax network), or anysuitable combination thereof The network 190 may be formed in whole orin part as an ad hoc network at the time of or proximate to the time ofcommunication between the user device 130 and the wearable device. Anyone or more portions of the network 190 may communicate information viaa transmission medium. As used herein, “transmission medium” refers toany intangible (e.g., transitory) medium that is capable ofcommunicating (e.g., transmitting) instructions for execution by amachine (e.g., by one or more processors of such a machine), andincludes digital or analog communication signals or other intangiblemedia to facilitate communication of such software.

FIG. 2 is a block diagram illustrating a mobile device 200 capable ofcapturing image data, according to some example embodiments. Forexample, the mobile device 200 may be an implementation of the userdevice 130. The mobile device 200 is configured to perform any one ormore of the methodologies discussed herein with respect to the userdevice 130. For example, the mobile device 200 can receive image data,determine image parameters, communicate with the wearable device toreceive data (e.g., sensor data) from the wearable device prior tocapturing an image, and modify image parameters based on data receivedfrom the wearable device. Further, the mobile device 200 can include foran image capture system discussed below with regard to FIG. 3.

The mobile device 200 can include a processor 202. In some embodiments,the processor 202 may be implemented as one or more processor 202. Theprocessor 202 can be any of a variety of different types of commerciallyavailable processors suitable for mobile devices 200 (for example, anXScale architecture microprocessor, a Microprocessor without InterlockedPipeline Stages (MIPS) architecture processor, or another type ofprocessor). A memory 204, such as a random access memory (RAM), a Flashmemory, or other type of memory, is typically accessible to theprocessor 202. The memory 204 can be adapted to store an operatingsystem (OS) 206, as well as application programs 208, such as a mobilelocation enabled application that can provide location-based services toa user. The processor 202 can be coupled, either directly or viaappropriate intermediary hardware, to a display 210 and to one or moreinput/output (I/O) devices 212, such as a keypad, a touch panel sensor,a microphone, an image capture device 213, and the like. The imagecapture device 213 can form a portion of an image capture system 300,described with respect to FIG. 3. The image capture system 300 canreceive image data, from the image capture device 213; receive data fromthe wearable device; and modify image parameters based on the datareceived from the wearable device.

In some example embodiments, the processor 202 can be coupled to atransceiver 214 that interfaces with an antenna 216. The transceiver 214can be configured to both transmit and receive cellular network signals,wireless data signals, or other types of signals via the antenna 216,depending on the nature of the mobile device 200. Further, in someconfigurations, a GPS receiver 218 can also make use of the antenna 216to receive GPS signals.

It should be noted that, in some embodiments, the mobile device 200 caninclude additional components or operate with fewer components thandescribed above. Further, in some embodiments, the mobile device 200 maybe implemented as a camera with some or all of the components describedabove with respect to FIG. 2. For example, the mobile device 200 can bea point and shoot digital camera, a digital single-lens reflex camera(DSLR), a film single-lens reflex camera (SLR), or any other suitablecamera capable of performing at least a portion of the methodologiesdescribed in the present disclosure.

The mobile device 200 can be configured to perform any one or more ofthe methodologies discussed herein. For example, the memory 204 of themobile device 200 may include instructions comprising one or moremodules for performing the methodologies discussed herein. The modulescan configure the processor 202 of the mobile device 200, or at leastone processor where the mobile device 200 has a plurality of processors,to perform one or more of the operations outlined below with respect toeach module. In some embodiments, the mobile device 200 and the servermachine 110 can each store at least a portion of the modules discussedabove and cooperate to perform the methods of the present disclosure, aswill be explained in more detail below.

FIG. 3 is a block diagram illustrating modules of an image capturesystem 300 capable of capturing image data with an image parameter,according to some example embodiments. The image capture system 300, asdiscussed above can be included in the mobile device 200 and incorporatethe image capture device 213. In some embodiments, in addition to theimage capture device 213, the image capture system 300 can include,temporarily or permanently, a processor (e.g., an image processor incommunication with the image capture device 213), a memory (e.g., thememory 204), and communications components (e.g., transceiver 214,antenna 216, and GPS receiver 218) in the form of one or more modulesperforming the methodologies described below.

The image capture system 300 includes a receiver module 310, adetermination module 320, a modification module 330, a capture module340, and a communication module 350, all configured to communicate witheach other (e.g., via a bus, shared memory, or a switch). Althoughdescribed as part of the mobile device 200, the image capture system 300can be distributed across multiple devices, such that certain of themodules associated with the image capture system 300 can reside in themobile device 200 and certain other modules can reside the wearablecomputing device 150 or 160. The modules, whether on a single device ordistributed across multiple systems or devices, can cooperate to performany one or more of the methodologies described herein.

Any one or more of the modules described herein may be implemented usinghardware (e.g., one or more processors of a machine) or a combination ofhardware and software. For example, any module described herein mayconfigure a processor (e.g., among one or more processors of a machine)to perform the operations described herein for that module. Moreover,any two or more of these modules may be combined into a single module,and the functions described herein for a single module may be subdividedamong multiple modules. Furthermore, as described above and according tovarious example embodiments, modules described herein as beingimplemented within a single machine, database, or device may bedistributed across multiple machines, databases, or devices. Forexample, as referenced above with respect to FIG. 1, in someembodiments, the server machine 110 can cooperate with the user device130 via the network 190 to perform the methods described below. In someembodiments, one or more of the modules or portions of the modules,discussed above, can be stored on the server machine 110 as well as onthe mobile device 200.

The receiver module 310 receives image data of a photo to be taken anddata from the wearable computing device 150 or 160. The image data cancomprise a subject. The receiver module 310 can also receive data fromthe wearable computing device 150 or 160 positioned proximate to thesubject. The receiver module 310 can receive the image data from theimage capture device 213 of the user device 130. For example, the imagecapture device 213 can comprise hardware components including an imagesensor (e.g., charge-coupled device (CCD) or complementarymetal-oxide-semiconductor (CMOS)). The image sensor can detect the imagedata of the photograph to be taken, and the image capture device 213 canpass the image data to the receiver module 310. In some embodiments, thereceiver module 310 can comprise a portion of the image capture device213, or the image capture device 213 can comprise a portion of thereceiver module 310. Further, in some instances, the receiver module 310can communicate with the image capture device 213 and the determinationmodule 320 via the communication module 350. The receiver module 310 canbe a hardware-implemented module. An example embodiment of components ofthe receiver module 310 is described with respect to the moduledescribed below and referenced by FIG. 7.

The data from the wearable computing device 150 or 160 received by thereceiver module 310 can comprise one or more of sensor data, dataindicative of user input, or other data capable of being generated ortransmitted by the wearable computing device 150 or 160. For example,where the data is sensor data, the data can be generated by a sensorsuch as a light sensor, a position sensor (e.g., GPS, altimeter, orelevation sensor), a clock, an accelerometer, a gyroscope, a microphone,or other sensors of the wearable computing device 150 or 160. In someembodiments, the receiver module 310 can receive sensor data related toan image to be captured while sensor data of the wearable computingdevice 150 or 160 unrelated to image data is not transmitted to thereceiver module 310. Where the data is user input data, the data can berepresentative of one or more user interaction with user interfaceelements of a user interface on the wearable computing device 150 or160.

The image to be captured can be image data already captured by the imagecapture device 213, where the methods described herein are relating todetermining whether to modify the image data or to recapture the imagedata based on data received from the wearable computing device 150 or160. In some embodiments, the image capture device 213 can receive imagedata and pass the image data to the display 210 without storing theimage data into the memory 204 of the mobile device 200.

The determination module 320 determines an image parameter based on theimage data. In some example embodiments, the determination module 320determines an image capture mode based on the image data. The imagecapture mode can comprise a portion or all of the image parameters forthe image data. The determination module 320 can receive the image datafrom the receiver module 310. For instance, the receiver module 310 canpass the image data to the determination module 320 via thecommunication module 350. The image capture mode can include a pluralityof predetermined settings or values for camera parameters which can beapplied to image data by the image capture device 213 at the time ofcapturing the image data. The determination module 320 can be a hardwareimplemented module as described in more detail below with and referencedby FIG. 7.

The determination module 320 can determine the image parameter based onthe image data received from the receiver module 310. For example, theimage capture device 213 can initially include one or more defaultsettings for image parameters, such as a default image size, a zoomvalue, white balance, color balance, focus value, frame orientation, orother image parameters discussed in more detail below. One or more ofthe default settings for image parameters can be included in the imagedata received by the determination module 320. In some exampleembodiments, the determination module 320 can determine the imageparameter based on a comparison between the image data and a set ofpredetermined default image parameters. In this example, thedetermination module 320 can determine the one or more image parameterwhere the value of an image parameter differs from that of thepredetermined default image parameters. In some instances, thedetermination module 320 can determine the image parameter based on ananalysis of the image data or metadata associated with the image data byidentifying one or more image parameters, described in more detailbelow, and values associated with the one or more image parameters.Although a few examples of methods for determining image parameters havebeen described, it will be understood by one skilled in the art that theimage parameters may be determined from the image data in any suitablemanner.

The determination module 320 can determine the image capture modesimilarly to the image parameter, by evaluation of the image data,evaluation of the metadata associated with the image data, a comparisonto a predetermined image capture mode, or other methods. The imagecapture mode can be a first image capture mode of a plurality of imagecapture modes. In some instances, the determination module 320 canselect or identify the image capture mode when the image capture modehas not already been specified by the image capture device 213 or theuser 132.

The modification module 330 modifies the image parameter based on thedata received from the wearable computing device 150 or 160. Forexample, the data received from the wearable computing device 150 or 160can include data from sensors related to one or more image parameter,such as a light sensor (e.g., exposure related image parameters), anaccelerometer (e.g., ISO speed and shutter speed image parameters), aposition sensor (e.g., giving relative position of the wearable deviceto the image capture system 300). In some instances, the data receivedfrom the wearable computing device 150 or 160 which can be correlatedwith other data to modify one or more image parameter. In embodimentswhere the determination module 320 determines the image capture mode forthe image capture device 213 capturing the image data, the modificationmodule 330 can receive data relating to one or more image parametershaving values affected by the image capture mode. The modificationmodule 330 can be a hardware implemented module as described in moredetail below and referenced by FIG. 7.

The modification module 330 can modify the image parameter by changingone or more values corresponding to the image parameter within the imagedata or within metadata associated with the image data. For example,where the modification module 330 receives data indicative of a lightreading from a light sensor, the modification module 330 can modify animage parameter by changing one or more values corresponding to a whitebalance, a shutter speed, an aperture size, or other image parametersaffected by a light proximate to the subject of an image to be captured.The values corresponding to the image parameter can be stored in a datastructure associated with the image data or comprising all or a portionof the image data. In some embodiments, where the image parameter is acamera parameter (e.g., lens parameters, aperture size, field of view,yaw, pitch, roll, a flash setting, and zoom parameters), themodification module 330 can, at least temporarily, modify the cameraparameter by changing one or more value corresponding to the cameraparameter in a data structure associated with the image capture device213.

The modification module 330 can modify the image capture mode, similarlyto the image parameter, by changing one or more values corresponding tothe image capture mode within the image data, within metadata associatedwith the image data, or within data associated with the image capturedevice 213. The modification module 330 can modify the image capturemode such that images captured by the image capture device 213 areprocessed using the modified image capture mode, for example. In someinstances, the modification module 330 can modify the image capturedevice 213, selecting a second or different image capture mode,regardless or in spite of the first image capture mode being selected bythe image capture device 213 or the user 132. In some exampleembodiments, the modification module 330 can modify the image capturedevice 213 based on a determination by the determination module 320 thatthe first image capture mode will produce an image unsuitable to one ormore of the image parameter or the subject of the photograph to betaken. In some embodiments, the modification module 330 can modify imageparameters (e.g., metadata relating to the image capture device 213 orthe image data) and then cause a recapturing of the image. In someembodiments, the modification module 330 can reprocess image dataalready captured by the image capture device 213 to conform to themodified image parameters or the image capture mode.

The capture module 340 captures the image data using the modified imageparameter. In embodiments where the modification module 330 has modifiedthe image capture mode, the capture module 340 captures the image datausing the modified image capture mode. The capture module 340 can be ahardware-implemented module as described in more detail below withrespect to the module of FIG. 7. In some instances the capture module340 can comprise all or a portion of the image capture device 213 andcomputer-executable instructions associated with the image capturedevice 213. In some example embodiments, the image capture device 213can include all or a portion of the capture module 340. The capturemodule 340 is in communication with the communication module 350. Forexample, the modified image parameter, the modified image capture mode,or data indicative of the modified image parameter or the modified imagecapture mode can be transmitted from the determination module 320 or themodification module 330 to the capture module 340 by the communicationmodule 350.

In some embodiments, the capture module 340 can capture image datarepresentative of the image to be captured (e.g., using an image sensorof the image capture device 213), process the image data using themodified image parameter or the modified image capture mode, and storethe processed image data on non-transitory machine-readable storagemedium associated with one or more of the user device 130 and thewearable computing device 150 or 160. For example, the image capturedevice 213 can capture image data for the image to be captured (e.g.,capture the image data and store the image data in the memory 204 of themobile device 200). The image data can be in a raw state (e.g., rawimage format) where the data has been minimally processed by the imagesensor or other components of the image capture system 300 or the mobiledevice 200. The capture module 340 can process the image by adjustingone or more of the exposure, contrast, color and detail, a filter, agradient, or other aspects of the image data based on the modified imageparameter. The capture module 340 can store the processed image data onthe non-transitory machine-readable storage medium via the communicationmodule 350.

The communication module 350 enables communication between the imagecapture system 300 and the wearable computing device 150 or 160. In someexample embodiments, the communication module 350 can enablecommunication among the receiver module 310, the determination module320, the modification module 330, and the capture module 340. Thecommunication module 350 can be a hardware-implemented module asdescribed in more detail below and referenced by FIG. 7. For example,the communication module 350 can include communications mechanisms suchas an antenna, a transmitter, one or more bus, and other suitablecommunications mechanisms capable of enabling communication between themodules, the user device 130, and the wearable computing device 150 or160.

FIG. 4 is a flow chart illustrating operations of the image capturesystem 300 in performing a method 400 of determining an image parameterof an image to be captured and modifying the image parameter based ondata received from a wearable computing device 150 or 160. Operations inthe method 400 may be performed by the image capture system 300, incooperation with one or more of the user device 130, the image capturedevice 213, and a wearable computing device 150 or 160, using modulesdescribed above with respect to FIG. 2.

In operation 410, the receiver module 310 receives image data of animage to be captured. The image capture system 300, via the receivermodule 310, can receive the image data prior to capturing the image as aphotograph. The image data can comprise at least one subject. In someinstances, the receiver module 310 receives the image data from theimage capture device 213 of the user device 130. For example, the imagecapture device 213 can detect image data using an image sensor, or othersuitable image sensor. Prior to capturing the image, the image sensorcan transmit the image data to the image capture device 213, which maythen transmit the image data to the receiver module 310. The image datacan be transmitted between the image capture device 213 and the receivermodule 310 via the communication module 350, via a direct physical orlogical connection between the image capture device 213 and the receivermodule 310, or by a portion of the image capture device 213 and thereceiver module 310 being shared between the image capture device 213and the receiver module 310.

Although described in reference to a still image, the image data can berepresentative of a set of image data and audio data to be captured(e.g., a video recording). In these instances, the receiver module 310can receive first image data representative of the set of image data andaudio data to be received. During capture of the set of image data andaudio data, the method 400 can be performed multiple times during thevideo recording to determine whether image parameters, image capturemodes, or camera parameters should be modified based on changes in theset of image data and audio data received by the receiver module 310.For example, the method 400 can be performed multiple times and modifyaspects of the set of image data and audio data based on changes inlight, movement, or sound associated with the subjects being captured inthe video recording.

In operation 420, the determination module 320 determines an imageparameter based on the image data. For example, the image parameter canbe one or more of an exposure time, an International Organization forStandardization (ISO) film speed, an exposure value, an exposure offset,a focus value (e.g., focus distance, continuous focus, automatic focus,manual focus, macro, infinity, and fixed), and other suitable imageparameters. The image parameter is not limited to parameters related tothe image data but can also correspond to a camera parameter such as aset of lens parameters (e.g., focal length, maximum aperture, wide anglelens, long-focus lens, normal lens, telephoto lens, a number of lenselements and degree of asphericity, lens filters), aperture size, fieldof view, yaw, pitch, roll, a flash setting, a color balance, a whitebalance, contrast ratio, dynamic range (e.g., f-stops), flare, blendpriority, view point offset, shift parameters, shear parameters, imagesensor parameters (e.g., image sensor size, image sensor ratio, andimage sensor pixel count) and image effects (e.g., sepia tones, blackand white, negative, solarize, and red-eye reduction).

The camera parameter can be a parameter associated with the imagecapture device 213, an image sensor (e.g., CCD or CMOS) of the imagecapture device 213, a lens of the user device 130, or other parameterassociated with the user device 130 or with components associated withthe image capture system 300. Although, in some embodiments, the cameraparameter cannot be directly changed, an effect of the camera parametercan be modified by a module described herein; software associated withthe image capture device 213; hardware or software of the user device130; a combination of hardware and software; or a combination of amodule, hardware, and software. For example, the camera parameterscorresponding to zoom capabilities of a lens of the image capture device213 can be modified by digital zoom capabilities associated withmodules, hardware, or software associated with the image capture device213.

The determination module 320 can determine the image parameter byreceiving the image parameter as part of the image data. For example,the determination module 320 can receive the image parameter as adefault setting for image parameters. In some instances the imageparameter can be determined based on a comparison between the image dataand a set of predetermined default image parameters. In comparing theimage data and the predetermined default image parameters, thedetermination module 320 can determine the image parameter where thevalue of the image parameter differs from the default. The determinationmodule 320 can also determine the image parameter using metadataassociated with the image data, received by the receiver module 310along with the image data and then transmitted to the determinationmodule 320.

In some example embodiments, the determination module 320 can determinethe image parameter based on a combination of image parameters receivedby the receiver module 310 and known to the determination module 320.For example, where three or more image parameters are associated and twoof the three or more image parameters are known, the determinationmodule 320 can calculate the remaining one or more image parametersbased on one or more appropriate formula.

Where the image parameter is a focus value, for example, the focus valuecan be indicative of the focus on the subject, or another point withinthe image data. The focus value can be a value associated with thesubject indicative of a point where light rays originating from thesubject converge. In some instances, the focus value can be a valueindicative of a principle focus or focal point at which the subject canbe reproduced in the image data above a predetermined level of clarityor sharpness.

In operation 430, the receiver module 310 receives data from a wearablecomputing device 150 or 160 positioned proximate to the subject.Receiving the data from the wearable computing device 150 or 160 cancomprise receiving sensor data generated by one or more sensor of thewearable computing device 150 or 160 and transmitted to the receivermodule 310 by a combination of the wearable computing device 150 or 160and the communication module 350. The one or more sensor can be chosenfrom a group consisting of a light sensor, a position sensor (e.g., GPS,altimeter, or elevation sensor), a clock, an accelerometer, a gyroscope,a microphone (e.g., where the user device 130 is configured to capturethe image data as a set of images comprising a video), or other sensorsor input devices of the wearable computing device 150 or 160.

In some example embodiments, a first portion of the data received fromthe wearable computing device 150 or 160 can be sensor data and a secondportion of the data received from the wearable computing device 150 or160 can be control data indicative of user input into the wearablecomputing device 150 or 160. The control data can include manualselections of user interface options or preferences including imageparameters and image capture modes. The control data can also includeselections among image capture and video recording.

In some instances, the data received from the wearable computing device150 or 160 can comprise determining a proximity of the wearablecomputing device 150 or 160 to the image capture device 213. In theseinstances, the method 400 can include operations in which the userdevice 130 and the wearable computing device 150 or 160 can communicateto determine a distance extending between the wearable computing device150 or 160 and the image capture device 213. Determining the proximityof the wearable computing device 150 or 160 can be performed bycomparison of GPS signals associated with the wearable computing device150 or 160 and the image capture device 213, determination based oncommunication technologies (e.g., Bluetooth® range estimation),triangulation, or any other suitable distance determination orestimation.

Although operation 430 is discussed with respect to receiving data froma single wearable computing device 150 or 160, the receiver module 310can receive data from a plurality of wearable computing devices. Forexample, where the image to be captured includes a plurality ofsubjects, some of whom are in proximity to a wearable computing device150 or 160 capable of or configured to communicate with the receivermodule 310, the receiver module 310 can receive data from each of thewearable computing devices 150 and 160 proximate to the plurality ofsubjects.

In operation 440, the modification module 330 modifies the imageparameter. The modification of the image parameter can generate amodified image parameter based on the data received from the wearablecomputing device 150 or 160. For example, the image data can comprise afirst subject and a second subject with the wearable device positionedproximate to the first subject. Where a plurality of wearable computingdevices are present, the modification module 330 can modify the imageparameter based on the data received from the plurality of wearablecomputing devices. For instance, the modification module 330 canprioritize data from a single representative wearable computing device150 or 160, where the data received from the plurality of wearablecomputing devices is with within a predetermined threshold. By way ofanother example, the modification module 330 can modify the imageparameter based on the data from each of the plurality of wearablecomputing devices or based on representative data (e.g., an averagevalue or a mean value) from the plurality of wearable computing devices.In some embodiments, the modification module 330 can modify a pluralityof image parameters, where certain or all of the plurality of imageparameters are modified based on data from certain or all of theplurality of wearable computing devices.

The image parameter can be modified to prioritize the first subjectproximate to the wearable computing device 150 or 160 based on the datareceived from the wearable computing device 150 or 160. For instance theimage parameter can be a focus value and modified to focus on the firstsubject instead of an original setting focusing on both the first andsecond subjects. Modifying the image parameter can comprise modifyingthe focus value based on the proximity of the wearable device. By way ofanother example, the image parameter can correspond to lighting to bemodified to ensure proper lighting for the first subject over the secondsubject.

The image parameter can also be modified based on the control data ofthe user input, at least in part. Where the receiver module 310 receivesboth sensor data and control data from the wearable computing device 150or 160, the image parameter can be modified in part based on the sensordata received from the wearable computing device 150 or 160 and in partbased on the control data received from the wearable computing device150 or 160. For example, the control data can cause the modificationmodule 330 to change a first image parameter, a focus setting, frommanual focus to automatic focus. The modification module 330 can changea second related image parameter, a focus value, to focus on a subjectproximate to the wearable computing device 150 or 160 based on thesensor data. By way of further example, the control data can cause themodification module 330 to change a single image parameter (e.g., whitebalance) to within a predetermined range and the sensor data can causethe modification module 330 to further refine the predetermined range toa smaller range or a single value.

In operation 450, the capture module 340 captures the image data (e.g.,the image to be captured) using the modified image parameter. Forinstance, the capture module 340 can be a portion of the image capturedevice 213 and cause the image sensor of the image capture device 213 tocapture the image data using the modified image parameter and store thecaptured image data on a non-transitory machine-readable storage mediumassociated with the image capture device 213. Where the capture module340 is not part of the image capture device 213, the capture module 340can communicate with the image capture device 213 via the communicationmodule 350 to cause the image capture device 213 to capture the imagedata.

In some embodiments, the capture module 340 can generate a processor oruser interface interrupt to cause the image capture device 213 tocapture the image data. The capture module 340 can then receive theimage data from the image capture device 213 and store the image data onthe non-transitory machine-readable storage medium.

FIG. 5 is a flowchart illustrating operations of the image capturesystem 300 in performing a method 500 of determining an image parameterof an image to be captured and modifying the image parameter based onsensor data received from the wearable computing device 150 or 160 andsensor data received from a sensor associated with the image capturesystem 300, the image capture device 213 or the user device 130.Operations in the method 500 may be performed by the image capturesystem 300, using modules described above with respect to FIG. 3. Asshown in FIG. 5, the method 500 can include one or more of theoperations of the method 400.

In operation 510, the receiver module 310 receives image data of animage to be captured. The image data comprises one or more subject. Theoperation 510 can be performed similarly to operation 410. For instance,the operation 510 can be performed by the receiver module 310 receivingthe image data from the image capture device 213 via the communicationmodule 350 prior to the image being captured and stored innon-transitory machine-readable storage media associated with the imagecapture device 213.

In operation 520, the determination module 320 determines an imageparameter based on the image data. In some embodiments, the operationcan be performed similarly to the operation 420. For example, thedetermination module 320 can determine the image parameter by receivingthe image parameter within the image data, receiving the image parameteras part of metadata associated with the image data, or by comparing theimage data with predetermined image parameter values.

In operation 530, the receiver module 310 receives data from a wearablecomputing device 150 or 160 positioned proximate to the subject. Theoperation 530 can be performed similarly to the operation 430. In someembodiments, the data from the wearable computing device 150 or 160 cancomprise sensor data, control data, or combinations thereof. The datafrom the wearable computing device 150 or 160 can be received by thereceiver module 310 via one or more signals received by thecommunication module 350.

In operation 540, the receiver module 310 receives data from a sensorassociated with the image capture system 300. In some exampleembodiments, the receiver module 310 can receive sensor data from one ormore sensors internal to the user device 130 or one or more sensorsexternal to the user device 130. The receiver module 310 can receive thesensor data directly from the one or more sensor, or from thecommunication module 350 in communication with the one or more sensor.Where the receiver module 310 receives sensor data from a plurality ofsensors associated with the user device 130, a portion of the sensordata can be received from the one or more sensor external to the userdevice 130 and a portion of the sensor data can be received from the oneor more sensor internal to the user device 130. For instance, the one ormore external sensor can be a periphery device associated with the imagecapture device 213, such as a light meter, a GPS device, a microphone,an external lens, or any other suitable periphery device.

In operation 550, the modification module 330 modifies the imageparameter. The modification of the image parameter generates a modifiedimage parameter based on a combination of the data received from thewearable computing device 150 or 160 and the data received from thesensor associated with the image capture system. In some exampleembodiments, the operation 550 can be performed similarly to theoperation 440. For instance, where the one or more sensor associatedwith the user device 130 is a light sensor, and the sensor data from thewearable computing device 150 or 160 is sensor data from a light sensorof the wearable computing device 150 or 160, the modification module 330can modify the image parameter (e.g., white balance, color balance,aperture size, or other parameter relating to light) to a value suitableto a combination of the sensor data from the sensor associated with theuser device 130 and the sensor of the wearable computing device 150 or160.

In operation 560, the capture module 340 captures the image data usingthe modified image parameter. In some embodiments, the operation 560 canbe performed similarly to the operation 450, by the capture module 340generating an interrupt causing the image capture device 213 to capturethe image data. The capture module 340 can then store the captured imagedata in a non-transitory machine readable storage medium associated withthe image capture device 213.

FIG. 6 is a flowchart illustrating operations of the image capturesystem 300 in performing a method 600 of determining an image parameterand a capture mode of an image to be captured and modifying the imageparameter and the capture mode based on data received from the wearablecomputing device 150 or 160. Operations in the method 600 may beperformed by the image capture system 300, using modules described abovewith respect to FIG. 3. As described herein, the method 600 can includeone or more of the operations of the method 400 or the method 500.

In operation 610, the receiver module 310 receives image data of animage to be captured. The image data comprises at least one subject. Theoperation 610 can be performed similarly to the operation 410 and 510with the receiver module 310 receiving image data from the image capturedevice 213 directly or via the communication module 350.

In operation 620, the determination module 320 determines an imageparameter based on the image data. The operation 620 can be performedsimilarly to the operation 420 or the operation 520. For example, thedetermination module 320 can determine the image parameter based on oneor more of a parameter value included in the image data, a parametervalue included in metadata associated with the image data, a comparisonof a received image parameter and a set of predetermined image parametervalues or settings. In some example embodiments, the determinationmodule 320 can also determine the image parameter based on two or moreknown image parameters related to the image parameter to be determined.

In operation 630, the determination module 320 determines an imagecapture mode of the image capture device 213 associated with the userdevice 130 based on the image data. For example, the image capture modecan be determined based on the image parameter to select a first imagecapture mode from a predetermined set of image capture modes. The imagecapture mode can also be determined based on the subject in the imagedata. The determination module 320 can determine the image capture modebased on an evaluation of the image data, evaluation of the metadataassociated with the image data, a comparison to a predetermined imagecapture mode, or other suitable methods.

The image capture mode can include a plurality of predetermined settingsor values for camera parameters (e.g., parameters of the image capturedevice 213) which can be applied to captured image data by the imagecapture device 213 at the time of capturing the image data. Forinstance, the image capture mode can include a predetermined set ofimage parameters or values for image parameters. For example, the imagecapture mode can include one or more filters, an aspect ratio, a fieldof view, an exposure time, an ISO film speed setting, a flash setting,and a color balance. The image capture mode can cooperate with one ormore additional image parameters to enable processing of captured imagedata for a desired effect.

In operation 640, the receiver module 310 receives data from a wearablecomputing device 150 or 160 positioned proximate to the subject. Theoperation 640 can be performed similarly to the operation 430 or theoperation 530. As discussed above, the data from the wearable computingdevice 150 or 160 can comprise sensor data, control data, orcombinations thereof. The data from the wearable computing device 150 or160 can be received by the receiver module 310 via one or more signalsreceived by the communication module 350.

In operation 650, the modification module 330 modifies the image capturemode. The modification of the image capture mode generates a modifiedimage capture mode based (e.g., in response to) on the data receivedfrom the wearable computing device 150 or 160. For example, where theimage capture mode is the first image capture mode selected from thepredetermined set of image capture modes, the first image capture modecan be modified by selecting a second image capture mode from thepredetermined set of image capture modes based on the data received fromthe wearable computing device 150 or 160.

In some example embodiments, the image capture mode can include aplurality of image capture settings (e.g., predetermined modificationsto one or more image parameter). In these embodiments, the modificationmodule 330 can modify the image capture mode by determining one or moreimage capture settings to be modified from the plurality of imagecapture settings. The determination of the one or more image capturesettings to be modified can be based upon a difference between the datareceived from the wearable computing device 150 or 160 and data receivedfrom the image capture device 213. Based on the determination, themodification module 330 can change a value associated with the one ormore image capture settings to be modified. For example, the imagecapture device 213 can indicate a white balance value contraindicatedfor proper exposure of the image data by data received from the wearablecomputing device 150 or 160. The white balance value can be changedbased on the contraindication.

The modification module 330 can also modify the image capture mode andthe image parameter, generating a modified image parameter, in responseto the data received from the wearable computing device 150 or 160. Forexample, the modification module 330 can modify the image capture mode,as described above, and modify the image parameter, which is related tothe image capture mode but not affected by the modification to the imagecapture mode.

In operation 660, the capture module 340 captures the image data usingthe modified image parameter and the modified image capture mode. Theoperation 660 can be performed similarly to the operation 450 or theoperation 560, with the inclusion of processing the captured image datausing the modified image capture mode or capturing the image data usingsettings associated with the modified image capture mode.

According to various example embodiments, one or more of themethodologies described herein may facilitate capture of image data withimage parameters based on data received from a wearable computing deviceor a combination of data from a device capturing the image data and datafrom the wearable computing device. Moreover, one or more of themethodologies described herein may facilitate capture of image data withimage parameters and image capture modes based on data received from awearable computing device or a combination of data received from thewearable computing device and data from the device capturing the imagedata. Hence, one or more of the methodologies described herein mayfacilitate capturing images with generally desirable image parametersand capture parameters to enable automated or semi-automated parameteradjustments appropriate for ambient conditions surrounding the subjectsto be photographed.

When these effects are considered in aggregate, one or more of themethodologies described herein may obviate a need for certain efforts orresources that otherwise would be involved in setting image parametersor image capture modes for capturing an image with desired image aspects(e.g., focus, color balance, aspect ratio, filters). Efforts expended bya user in determining image parameters or capture modes appropriate forambient conditions in an environment surrounding a subject of an imageto be captured may be reduced by one or more of the methodologiesdescribed herein. Computing resources used by one or more machines,databases, or devices (e.g., within the network environment 100) maysimilarly be reduced. Examples of such computing resources includeprocessor cycles, network traffic, memory usage, data storage capacity,power consumption, and cooling capacity.

FIG. 7 is a block diagram illustrating components of a machine 700,according to some example embodiments, able to read processor executableinstructions 724 from a machine-readable medium 722 (e.g., anon-transitory machine-readable medium, a machine-readable storagemedium, a computer-readable storage medium, or any suitable combinationthereof) and perform any one or more of the methodologies discussedherein, in whole or in part. Specifically, FIG. 7 shows the machine 700in the example form of a computer system (e.g., a computer) within whichthe instructions 724 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 700 toperform any one or more of the methodologies discussed herein may beexecuted, in whole or in part.

In alternative embodiments, the machine 700 operates as a standalonedevice or may be communicatively coupled (e.g., networked) to othermachines. In a networked deployment, the machine 700 may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a distributed (e.g.,peer-to-peer) network environment. The machine 700 may be a servercomputer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a cellular telephone, asmartphone, a set-top box (STB), a personal digital assistant (PDA), aweb appliance, a network router, a network switch, a network bridge, orany machine capable of executing the instructions 724, sequentially orotherwise, that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute the instructions 724 to perform all or part of any oneor more of the methodologies discussed herein.

The machine 700 includes a processor 702 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 704, and a static memory 706, which areconfigured to communicate with each other via a bus 708. The processor702 may contain microcircuits that are configurable, temporarily orpermanently, by some or all of the instructions 724 such that theprocessor 702 is configurable to perform any one or more of themethodologies described herein, in whole or in part. For example, a setof one or more microcircuits of the processor 702 may be configurable toact as one or more modules described herein.

The machine 700 may further include a graphics display 710 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine700 may also include an alphanumeric input device 712 (e.g., a keyboardor keypad), a cursor control device 714 (e.g., a mouse, a touchpad, atrackball, a joystick, a motion sensor, an eye tracking device, or otherpointing instrument), a storage unit 716, an audio generation device 718(e.g., a sound card, an amplifier, a speaker, a headphone jack, or anysuitable combination thereof), and a network interface device 720.

The storage unit 716 includes the machine-readable medium 722 (e.g., atangible and non-transitory machine-readable storage medium) on whichare stored the instructions 724 embodying any one or more of themethodologies or functions described herein. The instructions 724 mayalso reside, completely or at least partially, within the main memory704, within the processor 702 (e.g., within the processor's cachememory), or both, before or during execution thereof by the machine 700.Accordingly, the main memory 704 and the processor 702 may be consideredmachine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 724 may be transmitted orreceived over the network 190 via the network interface device 720. Forexample, the network interface device 720 may communicate theinstructions 724 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 700 may be a portable computingdevice, such as a smart phone or tablet computer, and have one or moreadditional input components 730 (e.g., sensors or gauges). Examples ofsuch input components 730 include an image input component (e.g., one ormore cameras), an audio input component (e.g., a microphone), adirection input component (e.g., a compass), a location input component(e.g., a global positioning system (GPS) receiver), an orientationcomponent (e.g., a gyroscope), a motion detection component (e.g., oneor more accelerometers), and an altitude detection component (e.g., analtimeter). Inputs harvested by any one or more of these inputcomponents 730 may be accessible and available for use by any of themodules described herein.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 722 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 724. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing the instructions 724 for execution by the machine700, such that the instructions 724, when executed by one or moreprocessors of the machine 700 (e.g., processor 702), cause the machine700 to perform any one or more of the methodologies described herein, inwhole or in part. Accordingly, a “machine-readable medium” refers to asingle storage apparatus or device, as well as cloud-based storagesystems or storage networks that include multiple storage apparatus ordevices. The term “machine-readable medium” shall accordingly be takento include, but not be limited to, one or more tangible (e.g.,non-transitory) data repositories in the form of a solid-state memory,an optical medium, a magnetic medium, or any suitable combinationthereof.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may be a moduletemporarily configured by software code stored or otherwise embodied ona machine-readable medium or in a transmission medium), hardwaremodules, or any suitable combination thereof. A “hardware module” is atangible (e.g., non-transitory) unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various example embodiments, one or more computer systems(e.g., a standalone computer system, a client computer system, or aserver computer system) or one or more hardware modules of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, and such a tangible entity may bephysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software mayaccordingly configure one or more processors, for example, to constitutea particular hardware module at one instance of time and to constitute adifferent hardware module at a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. As used herein,“processor-implemented module” refers to a hardware module in which thehardware includes one or more processors. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain operations may be distributed among the oneor more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

What is claimed is:
 1. A method comprising: receiving, by an imagecapture device from a wearable audio capture device worn by a user,first audio data comprising a first sound associated with a subject tobe captured in an image by the image capture device, the wearable audiocapture device being separate and external to the image capture device;modifying, based on the first audio data that is received by the imagecapture device, an audio parameter of the image capture device; andcapturing, by the image capture device based on the modified audioparameter, first image data comprising the subject and second audio datacomprising a second sound associated with the subject.
 2. The method ofclaim 1, wherein the first image data comprises audio data associatedwith the subject.
 3. The method of claim 1, wherein: the modifying theaudio parameter of the image capture device comprises modifying an imageparameter of the image capture device, and the capturing the first imagedata further comprises using the modified image parameter to capture thefirst image data.
 4. The method of claim 3, wherein the image parametercomprises at least one of a field of view, a yaw, a pitch, or a roll ofthe image capture device.
 5. The method of claim 1, further comprising:receiving, before receiving the first audio data, third audio dataindicative of a sound associated with the audio parameter, wherein theaudio parameter is modified in response to a difference between thefirst audio data and the third audio data.
 6. The method of claim 1,further comprising: capturing, before receiving the first audio data,second image data comprising the subject, wherein first image datacomprises a modified version of the second image data.
 7. The method ofclaim 1, further comprising modifying an image parameter of the imagecapture device based on a proximity of the wearable audio capture devicerelative to the image capture device.
 8. A non-transitorymachine-readable storage medium comprising processor-executableinstructions that, when executed by a processor of a machine, cause themachine to perform operations comprising: receiving, by an image capturedevice from a wearable audio capture device worn by a user, first audiodata comprising a first sound associated with a subject to be capturedin an image by the image capture device, the wearable audio capturedevice being separate and external to the image capture device;modifying, based on the first audio data that is received by the imagecapture device, an audio parameter of the image capture device; andcapturing, by the image capture device based on the modified audioparameter, first image data comprising the subject and second audio datacomprising a second sound associated with the subj ect.
 9. Thenon-transitory machine-readable storage medium of claim 8, wherein thefirst image data comprises audio data associated with the subject. 10.The non-transitory machine-readable storage medium of claim 8, wherein:the modifying the audio parameter of the image capture device comprisesmodifying an image parameter of the image capture device, and thecapturing the first image data further comprises using the modifiedimage parameter to capture the first image data.
 11. The non-transitorymachine-readable storage medium of claim 10, wherein the image parametercomprises at least one of a field of view, a yaw, a pitch, or a roll ofthe image capture device.
 12. The non-transitory machine-readablestorage medium of claim 8, wherein the operations further comprise:receiving, before receiving the first audio data, third audio dataindicative of a sound associated with the audio parameter, wherein theaudio parameter is modified in response to a difference between thefirst audio data and the third audio data.
 13. The non-transitorymachine-readable storage medium of claim 8, wherein the operationsfurther comprise: capturing, before receiving the first audio data,second image data comprising the subject, wherein first image datacomprises a modified version of the second image data.
 14. Thenon-transitory machine-readable storage medium of claim 8, wherein theoperations further comprise modifying an image parameter of the imagecapture device based on a proximity of the wearable audio capture devicerelative to the image capture device.
 15. A system, comprising: one ormore processors; and a non-transitory machine-readable storage mediumcoupled to the one or more processors, the non-transitorymachine-readable storage medium including instructions that, whenexecuted by the one or more processors, cause the one or more processorsto perform operations comprising: receiving, by an image capture devicefrom a wearable audio capture device worn by a user, first audio datacomprising a first sound associated with a subject to be captured in animage by the image capture device, the wearable audio capture devicebeing separate and external to the image capture device; modifying,based on the first audio data that is received by the image capturedevice, an audio parameter of the image capture device; and capturing,by the image capture device based on the modified audio parameter, firstimage data comprising the subject and second audio data comprising asecond sound associated with the subject.
 16. The system of claim 15,wherein the first image data comprises audio data associated with thesubject.
 17. The system of claim 15, wherein: the modifying the audioparameter of the image capture device comprises modifying an imageparameter of the image capture device, and the capturing the first imagedata further comprises using the modified image parameter to capture thefirst image data.
 18. The system of claim 17, wherein the imageparameter comprises at least one of a field of view, a yaw, a pitch, ora roll of the image capture device.
 19. The system of claim 15, whereinthe operations further comprise: modifying an image parameter of theimage capture device based on a proximity of the wearable audio capturedevice relative to the image capture device; and capturing, beforereceiving the first audio data, second image data comprising thesubject, wherein first image data comprises a modified version of thesecond image data.